Optimization of Multiproduct Batch Plant Design for Protein Production Using Gradient Boosting Algorithms

This work deals with the problem of modeling and prediction of compressive strength of concrete structure in multiproduct batch plant design of protein production found in a chemical engineering process with uncertain demand. Modeling the strength of concrete for this process is very complex. However, it can be solved by minimizing the investment cost. Therefore, the aim of this work is to minimize the investment cost and find out the number and size of parallel equipment units in each stage. For this purpose, it is proposed to solve the problem by using gradient boosting algorithms, could be interpreted as an optimization algorithm on a suitable cost function. Which take into account, the uncertainty on the demand using gaussian process modeling. The results about number and size of equipments investment cost, production time, process time and Idle times in plant obtained by gradient boosting algorithms are the best. This methodology can help the decision makers and constitutes a very promising framework for finding a set of “good solutions”.